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Curated links from external sources — not 360Softy original articles.

ExternalAI
NVIDIA Technical Blog

Maximize AI Factory Energy Efficiency Through Full-Stack Inference and Training Optimizations

Power can account for 40% of the operating expenses (OpEx) to run an AI factory. Each watt can be spent on overhead, data ingestion, training, or generating... Power can account for 40% of the operating expenses (OpEx) to run an AI factory. Each watt can be spent on overhead, data ingestion, training, or generating tokens for customers. And most sites are capped at a fixed power level provided by a regional provider. Under these conditions, performance per watt becomes a key efficiency metric th

NVIDIA Technical BlogRead original
ExternalCybersecurity
Krebs on Security

Scattered Spider Hackers Plead Guilty on Day 1 of Trial

Two men pleaded guilty in the United Kingdom this week to criminal charges stemming from an August 2024 cyberattack that crippled Transport for London, the entity responsible for the public transport network in the Greater London area. The duo were key members of a prolific cybercrime group known as Scattered Spider, and their guilty pleas came on the first day of what was expected to be a six-week trial.

Ne'er-Do-Well NewsRansomwareBBC
Krebs on SecurityRead original
ExternalSoftware Engineering
Meta Engineering

How Meta Engineered Ultra-Narrow Batteries for AI Glasses

Smart glasses like the Ray-Ban Meta and Oakley Meta Vanguards need to pack enough energy to power features like cameras, speakers, AI workloads, and even a display. But it all has to fit into the glasses’ temple arms. So how do you place a battery with enough power to run a pair of smart glasses [...] Read More... The post How Meta Engineered Ultra-Narrow Batteries for AI Glasses appeared first on Engineering at Meta.

CultureProduction EngineeringVirtual Reality
Meta EngineeringRead original
ExternalCloud
Google Cloud Blog

Log Analytics is now Observability Analytics: Query logs and traces with SQL

To effectively operate and troubleshoot applications, developers and site reliability engineers (SREs) need to understand the full context of their system's behavior, typically as part of their logging and observability tooling. Today, we’re excited to announce a variety of new capabilities in our Google Cloud Observability suite: Log Analytics is now Observability Analytics. Trace data within Observability Analytics is generally available (GA). The Observability API for management and configura

Management Tools
Google Cloud BlogRead original
ExternalCloud
Google Cloud Blog

Verifiable, private AI: Google Cloud expands Confidential Computing frontiers

Protecting sensitive data used with AI is a critical part of our commitment to providing advanced and secure cloud infrastructure. Confidential Computing cryptographically protects data in use in hardware-based Trusted Execution Environments (TEEs) with verifiable data integrity.  We are thrilled to share our latest Confidential Computing innovations across our hardware ecosystem that help further strengthen verifiable privacy in cloud AI deployments.  Confidential AI at global scale By scaling

AI & Machine LearningComputeSecurity & Identity
Google Cloud BlogRead original
ExternalSoftware Engineering
GitHub Blog

I automated my job (and it made me a better leader)

Explore how my day as a senior leader looks now that I use 40 automations to help, and learn more about some of my favorites. The post I automated my job (and it made me a better leader) appeared first on The GitHub Blog.

AI & MLCareer growthDeveloper skills
GitHub BlogRead original
ExternalSoftware Engineering
DZone

Connect Existing Data to AI Retrieval: How to Build Production-Ready Search Without Rebuilding Core Systems

Editor’s Note: The following is an article written for and published in DZone’s 2026 Trend Report, Cognitive Databases, Intelligent Data: Unified Infrastructure for Vector Search, AI-Optimized Queries, and Hybrid Workloads. Most teams that want to add AI retrieval already have the data they need in databases, document stores, ticketing systems, and lakehouse tables that serve their purpose well. You usually do not need to centralize or rebuild this data; you can add retrieval as a thin layer ove

DZoneRead original

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